Currently, packet data networks are widespread. Their architectural features allow constructing covert channels thatare able to transmit covert data under the conditions of using standard protection measures. However, encryption orpackets length normalization, leave the possibility for an intruder to transfer covert data via timing covert channels(TCCs). In turn, inter-packet delay (IPD) normalization leads to reducing communication channel capacity.Detection is an alternative countermeasure. At the present time, detection methods based on machine learning arewidely studied. The complexity of TCCs detection based on machine learning depends on the availability of trafficsamples, and on the possibility of an intruder to change covert channels parameters. In the current work, we explorethe cases of TCCs detection via machine learning and study the possibility to implement learning machinesalgorithms for detecting TCCs under conditions of varying covert channel characteristics: flow capacity andencoding scheme.